AI Is Reshaping Software Engineering — But There’s a Catch…

I don’t believe AI will replace software developers, but it will exponentially boost their productivity. The more I talk to developers, the more I hear the same thing—they’re now accomplishing in half the time what used to take them days.

But there’s a risk… Less experienced developers often take shortcuts, relying on AI to fix bugs, write code, and even test it—without fully understanding what’s happening under the hood. And the less you understand your code, the harder it becomes to debug, operate, and maintain in the long run.

So while AI is a game-changer for developers, junior engineers must ensure they actually develop the foundational skills—otherwise, they’ll struggle when AI can’t do all the heavy lifting.

Eduardo picks up on something I’ve been concerned about too: that the productivity boost afforded to junior developers by AI does not provide them with the necessary experience to be able to continue to advance their skills. GenAI for developers can be a dead end, from a personal development perspective.

That’s a phenomenon not unique to AI, mind. The drive to have more developers be more productive on day one has for many years lead to an increase in developers who are hyper-focused on a very specific, narrow technology to the exclusion even of the fundamentals that underpin them.

When somebody learns how to be a “React developer” without understanding enough about HTTP to explain which bits of data exist on the server-side and which are delivered to the client, for example, they’re at risk of introducing security problems. We see this kind of thing a lot!

There’s absolutely nothing wrong with not-knowing-everything, of course (in fact, knowing where the gaps around the edges of your knowledge are and being willing to work to fill them in, over time, is admirable, and everybody should be doing it!). But until they learn, a developer that lacks a comprehension of the fundamentals on which they depend needs to be supported by a team that “fill the gaps” in their knowledge.

AI muddies the water because it appears to fulfil the role of that supportive team. But in reality it’s just regurgitating code synthesised from the fragments it’s read in the past without critically thinking about it. That’s fine if it’s suggesting code that the developer understands, because it’s like… “fancy autocomplete”, which you can accept or reject based on their understanding of the domain. I use AI in exactly this way many times a week. But when people try to use AI to fill the “gaps” at the edge of their knowledge, they neither learn from it nor do they write good code.

I’ve long argued that as an industry, we lack a pedagogical base: we don’t know how to teach people to do what we do (this is evidenced by the relatively high drop-out rate on computer science course, the popular opinion that one requires a particular way of thinking to be a programmer, and the fact that sometimes people who fail to learn programming through paradigm are suddenly able to do so when presented with a different one). I suspect that AI will make this problem worse, not better.

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